The invention relates to a gene classification method and system based on clustering and random forest algorithms and belongs to the technical field of biological information. The method comprises a step of acquiring gene sample data, clustering the acquired gene sample data by using the clustering algorithm to obtain a cluster center, and supplementing a training sample set with an obtained cluster center set, a step of adjusting the number of fixed decision tree random description attributes in a traditional random forest algorithm to a random value, wherein on one hand, strong decision trees in a decision tree set are kept, on the other hand, the number of average random description attributes of the decision tree set is reduced, thus the correlation between the decision trees is further reduced, and a step of predicting genetic data to be classified by using each decision tree in a random forest model. According to the method and the system, the cluster center obtained through theclustering algorithm is taken as artificial data to expand the training set of the random forest model, thus the random forest model is fully trained, the obtained classification model has high precision, and the accuracy of the classification of genetic data is improved.